diff --git a/pyproject.toml b/pyproject.toml
index 78ba0e6..e056d1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,7 +17,7 @@ dependencies = [
     "openai>=1.0.0",
     "pydantic>=2.12.0",
     "pydantic-settings>=2.11.0",
-    "qdrant-client>=1.16.2",
+    "qdrant-client>=1.17.0",
     "uvicorn>=0.34.0",
 ]
 
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
index dbce3f1..aeeb47e 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
@@ -32,7 +32,7 @@ async def retrieve_papers_dense(self, query: str, top_k: int = 5) -> list[dict]:
         """
         if self.qdrant_client.cloud_inference:
             dense_vector = self.qdrant_client._define_openai_vectors(
-                query, dimensions=self.qdrant_client.embedding_dimension
+                query, mrl_dimensions=self.qdrant_client.embedding_dimension
             )
         else:
             dense_vector = await self.qdrant_client._get_openai_vectors(
@@ -76,10 +76,10 @@ async def retrieve_papers_hybrid(self, query: str, top_k: int = 5) -> list[dict]
         """
         if self.qdrant_client.cloud_inference:
             retriever_vector = self.qdrant_client._define_openai_vectors(
-                query, dimensions=self.qdrant_client.embedding_dimension
+                query, mrl_dimensions=self.qdrant_client.embedding_dimension
             )
             reranker_vector = self.qdrant_client._define_openai_vectors(
-                query, dimensions=self.qdrant_client.reranker_embedding_dimension
+                query, mrl_dimensions=self.qdrant_client.reranker_embedding_dimension
             )
         else:
             openai_vector = await self.qdrant_client._get_openai_vectors(
@@ -152,7 +152,7 @@ async def recommend_papers_based_on_constraints(
             if positive_examples:
                 positive_vectors = [
                     self.qdrant_client._define_openai_vectors(
-                        pos_example, dimensions=self.qdrant_client.embedding_dimension
+                        pos_example, mrl_dimensions=self.qdrant_client.embedding_dimension
                     )
                     for pos_example in positive_examples
                 ]
@@ -161,7 +161,7 @@ async def recommend_papers_based_on_constraints(
             if negative_examples:
                 negative_vectors = [
                     self.qdrant_client._define_openai_vectors(
-                        neg_example, dimensions=self.qdrant_client.embedding_dimension
+                        neg_example, mrl_dimensions=self.qdrant_client.embedding_dimension
                     )
                     for neg_example in negative_examples
                 ]
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
index cf586d1..de3955d 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
@@ -103,14 +103,14 @@ async def _get_openai_vectors(self, text: str, dimensions: int) -> list[float]:
             logger.error(f"❌ Failed to create embedding: {e}")
             raise
 
-    def _define_openai_vectors(self, text: str, dimensions: int = 1536) -> models.Document:
+    def _define_openai_vectors(self, text: str, mrl_dimensions: int = 1536) -> models.Document:
         """
         Wrap text in models.Document to handle OpenAI embeddings inference
         through Qdrant's Cloud.
 
         Args:
                 text (str): Input text.
-                dimensions (int): Number of dimensions for the embedding. https://platform.openai.com/docs/api-reference/embeddings/create#embeddings-create-dimensions
+                mrl_dimensions (int): Number of MRL dimensions for the embedding. https://platform.openai.com/docs/api-reference/embeddings/create#embeddings-create-dimensions
         Returns:
                 models.Document: Document object.
         """
@@ -119,7 +119,7 @@ def _define_openai_vectors(self, text: str, dimensions: int = 1536) -> models.Do
             model=f"openai/{settings.qdrant.embedding_model}",
             options={
                 "openai-api-key": settings.openai.api_key.get_secret_value(),
-                "dimensions": dimensions,
+                "mrl": mrl_dimensions,
             },
         )
 
@@ -243,10 +243,10 @@ async def upsert_points(
 
                     if self.cloud_inference:
                         retriever_vector = self._define_openai_vectors(
-                            abstract, dimensions=self.embedding_dimension
+                            abstract, mrl_dimensions=self.embedding_dimension
                         )
                         reranker_vector = self._define_openai_vectors(
-                            abstract, dimensions=self.reranker_embedding_dimension
+                            abstract, mrl_dimensions=self.reranker_embedding_dimension
                         )
                     else:
                         openai_vector = await self._get_openai_vectors(abstract, dimensions=self.reranker_embedding_dimension)  # MRL, https://platform.openai.com/docs/guides/embeddings#use-cases